Research on Wind-Solar-Thermal-Storage Configuration Schemes of DC Near-field Based on Time Sequential Simulation

Yan, Zhibin; Guo, Linrun; Li, Chi; Wang, Shuai; Sang, Zhencheng

doi:10.1109/cieec54735.2022.9845817

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2024

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“…Successful applications demonstrated their practical utility within specific contexts, emphasizing informed implementation. Yan et al [5] studied DC transmission prominence and introduced a comprehensive model for optimized wind and solar power output within system constraints. The model integrated demand-supply dynamics, various plant types, and DC transmission intricacies.…”

Section: Introductionmentioning

confidence: 99%

Predicting Photovoltaic Power Generation by Machine Learning Using Time Series Analysis

Nahar,

Rudro,

Sohan

et al. 2024

Preprint

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Negative externalities of fossil fuels together with adjuvant features of solar energy is driving the global espousal of solar energy technologies. This article presents a forecasting model for photovoltaic (PV) power generation using real-time data analysis of two solar plants through machine learning time series model (MLTSM). The work focuses on critical factors such as predictive accuracy, residual distribution, RMSE values, data quality, and model suitability for forecasting. The findings demonstrate that the predictive model achieves an accuracy of 98% for Plant 1 and 91% for Plant 2. Overall, the MLTSM exhibits its effectiveness in enhancing PV power generation forecasting, thereby contributing to the attainment of energy security.

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Section: Introductionmentioning

confidence: 99%